Our experimental results illustrate that the proposed method shows a robust image manipulation performance for the target attribute (see Fig. REF ). Furthermore, the sound-based approach supports more diverse and detailed image manipulation results related to audio intensity information than state-of-the-art text-based image manipulation methods. In addition, we demonstrate that our sound representation outperforms existing audio-visual joint representation [1]}, [2]}, [3]} for image manipulation.
We provide a wide range of image manipulation scenarios for real-world applications, such as music style transfer and sound-guided artistic painting manipulation.
This manuscript extends the preliminary conference version of this work [3]}. In this present study, our main contributions are listed as follows:
